Phenotype prediction has been performed using differentially expressed genes (DE genes) between different conditions or cell types. Here we present a method to predict cell types based on dimension reduction of gene expression data from single cells.
| groups | Freq |
|---|---|
| 1 | 9083 |
| 2 | 8977 |
| 3 | 526 |
| 4 | 201 |
0.05| Method | Significant eigenvectors | Cumulative variance |
|---|---|---|
| MDS | 14 | 24.13 |
| Method | Significant eigenvectors | Cumulative variance |
|---|---|---|
| PCA | 9 | 79.87 |
There seems to be no clear difference between the distributions of both clusters. This may explain why the prediction accuracy value is close to 0.5 (about 0.6, see Performance section) as the probability of one cell belonging to one cluster or other is almost equal. The increase of ~ 0.1 accuraccy value may be due to small regions of non-overlapped distributions which discriminate cluster identity. This accuraccy increment is related to the increase of sensitivity due to enrichment of target cluster (see isolated and marginal red lines) as the number of eigenvectors considered for prediction increases.
Dimension reduction methods exhibit better accuracy, specificity and kappa values when compared to the prediction based on DE genes.
| Method | Significant eigenvectors | Cumulative variance |
|---|---|---|
| MDS | 11 | 22.26 |
| Method | Significant eigenvectors | Cumulative variance |
|---|---|---|
| PCA | 8 | 3.29 |
Due to the abundance of isolated distributions of the negative class (cluster 134), the specificity increases (opposite to the behavior observed for cluster 1).
Dimension reduction methods exhibit better accuracy, sensitivity and kappa values when compared to the prediction based on DE genes.
| Method | Significant eigenvectors | Cumulative variance |
|---|---|---|
| MDS | 16 | 25.53 |
| Method | Significant eigenvectors | Cumulative variance |
|---|---|---|
| PCA | 17 | 80 |
| Method | Significant eigenvectors | Cumulative variance |
|---|---|---|
| MDS | 13 | 19.16 |
| Method | Significant eigenvectors | Cumulative variance |
|---|---|---|
| PCA | 12 | 79.66 |